Data Engineer — New York, NY

About The Role

The role owns the data infrastructure that powers analytics and product decisions - batch and streaming pipelines that move billions of events daily into a warehouse serving finance, product, and growth teams.

The team operates at a scale where pipeline reliability, data freshness, and cost efficiency all matter equally, and this role sits at the center of that: designing ingestion systems, modeling warehouse data, and partnering directly with analysts and backend engineers.

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines in SN4waAF and dbt, moving data from Kafka, Postgres, and third-party APIs into Snowflake

• Model warehouse data using dimensional modeling and dbt best practices, ensuring clean, testable, well-documented marts for downstream analytics

• Own data quality end-to-end: build validation and anomaly detection checks, set SLAs for pipeline freshness, and drive incident response when things break

• Optimize Spark and SQL jobs for performance and cost, tuning workloads running against petabyte-scale datasets

• Partner with data analysts and product teams to translate ambiguous business questions into robust data models and self-serve datasets

• Build and maintain streaming pipelines using Kafka and Flink (or Spark Structured Streaming) for real-time use cases

• Contribute to data platform tooling: CI/CD for pipelines, schema management, and observability dashboards

What We Are Looking For

• 3–7 years of experience in data engineering, analytics engineering, or backend engineering with heavy data focus

• Expert-level SQL and strong Python; comfort writing production-grade code with tests and code reviews

• Hands-on experience with a modern warehouse (Snowflake, BigQuery, or Redshift) and orchestration tooling (SN4waAF, Dagster, or Prefect)

• Practical experience with dbt and dimensional data modeling; understanding of incremental models, snapshots, and data lineage

• Experience with streaming or event-driven architectures (Kafka, Kinesis, or equivalent) at scale

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

• Bonus: experience with Flink or Kafka Streams, data quality tooling (Great Expectations, Monte Carlo), Terraform/IaC, or building metrics layers and semantic models